High Volume Global Data AnalysisAdatis harnesses the Twitter hash tag #nowplaying to harvest data generated by music consumers all over the world, to better understand global music consumption patterns.

ProblemThe increasing rate at which publicly available information is being produced and published creates opportunities as well as challenges, for data analysts and companies wishing to increase their competitive advantage by exploiting the business benefit locked away in these high volume and high velocity data-streams.

The music business has changed dramatically in the last 30 years moving from LP's to CD's to MP3 and massive piracy, to Streaming. Having a better understanding of what artists and musical genres people listen to in both a historical and real-time basis and across different countries and times of the day has massive benefits for the music industry, from recording companies and music publishers to radio stations and to the artists and songwriters themselves, many of whom are independent:- Which genres of music are popular at different times of the day and in which countries? - Which artists are listened to the most, what are the top 10 listened to artists (overall and within genre / country etc.)? - How many plays occur and how many listeners are there in any given time period? - Can we forecast play volumes even at an individual artist grain?

SolutionA large volume of data was harvested through a traditional Internet of Things (IoT) based Lambda architecture offering benefits of both Real Time and Batch data processing. The single Twitter hash tag #nowplaying was utilised and 'fuzzy' matched to pre-determined list of artists and genres with the processed results stored in a database. Using Power BI, Adatis transformed this tabular data into a series of exciting and informative interactive dashboards. Allowing users to discover insights that would otherwise not be possible to obtain.

Now users can easily:

- Make sense of a massive volume of data in seconds- Forecast play volumes across genre / artist / country- Track where listeners originate- View the musical genres and artists people listen to in different countries and cities- Understand how the time of the day affects the kind of music and artists people listen to- Locate actual listeners and clusters of listeners anywhere in the world